Method for measuring intracranial elasticity

ABSTRACT

A novel method to noninvasively measure intracranial pressure (ICP) and more generally brain elasticity is disclosed. ICP is determined using an algorithm coupled on a simulated artificial neural network (SANN) that calculates ICP based on a determination of a set of interacted ultrasound signals (IUSs) generated from multiple ultrasound pulses. The methods and systems of the present invention are capable of rapidly determining ICP without manual review of EPG waves by a technician.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/381,632 filed Dec. 29, 2011, now allowed, which is a National Phaseof PCT/US 09/052263 which claims benefit of U.S. Provisional PatentApplication No. 61/084,827 filed Jul. 30, 2008, which are herebyincorporated by reference as part of the present disclosure.

FIELD OF THE INVENTION

The present invention relates to a method for non-invasively measuringintracranial pressure.

BACKGROUND OF THE INVENTION

Generally, mammals such as humans have a constant intracranial volume ofblood and, therefore, a constant intracranial pressure (“ICP”). Avariety of normal and pathological conditions, however, can producechanges in intracranial pressure. Elevated intracranial pressure canreduce blood flow to the brain and in some cases can cause the brain tobecome mechanically compressed, and ultimately herniate. The most commoncause of elevated intracranial pressure is head trauma. Additionalcauses of elevated intracranial pressure include, but are not limited toshaken-baby syndrome, epidural hematoma, subdural hematoma, brainhemorrhage, meningitis, encephalitis, lead poisoning, Reye's syndrome,hypervitaminosis A, diabetic ketoacidosis, water intoxication, braintumors, other masses or blood clots in the cranial cavity, brainabscesses, stroke, ADEM (“acute disseminated encephalomyelitis”),metabolic disorders, hydrocephalus, and dural sinus and venousthrombosis. Because changes in intracranial pressure require constantmonitoring and possible surgical intervention, the development oftechniques to monitor intracranial pressure remains an important goal inmedicine. U.S. Pat. No. 6,875,176.

Conventional intracranial pressure monitoring devices include: epiduralcatheters; subarachnoid bolt/screws; ventriculostomy catheters; andfiberoptic catheters. All of these methods and systems are invasive, andrequire invasive surgical procedures by highly trained neurosurgeons.Moreover, none of these techniques are suited to rapid or regularmonitoring of intracranial pressure. In addition, all of theseconventional techniques measure ICP locally, and presumptions are madethat the local ICP reflects the whole brain ICP. The teachings of U.S.Pat. No. 6,875,176 illustrate these limitations of the existing methods.

There are no widely accepted methods of non-invasively measuring ICP.Clinically, however, the development of an effective means of measuringICP is very important as ICP can be predictive of clinical outcome, andcan lead to altered, more effective therapy. For example, aftertraumatic brain injury, intracranial pressure tends to rise requiringboth prompt recognition and treatment. Zanier et al. Critical Care 11:R7(“2007”). The existing standards in measuring ICP require direct,invasive measurement involving the placement of epidural transducers orintraventricular or intraparenchymatous catheters. Frank et al.Zentralbl Neurochir 61(“4”): 177-80 (“2000”). The use of invasivemethods increases the risk of injury from infection, bleeding orsurgical mishap. Czosnyka et al. J. Neurol. Neurosurg. Psychiatry 75:813-821 (“2004”).

A variety of different techniques for noninvasively measuring ICP havebeen explored, including, measuring otoacoustic emissions (“Frank et al.Zentralbl Neurochir 61(“4”): 177-80 (“2000”)”), and ultrasound with atranscranial Doppler (Ragauskas et al. Innovative non-invasive methodfor absolute intracranial pressure measurement [online], [retrieved onJul. 30, 2008]. Retrieved from the Internet <URL:http://www.neurosonology.org/bern2002/abs_12.html>).

For example, U.S. Pat. No. 6,702,743 (“the '743 patent”) discloses anon-invasive means of measuring ICP. An ultrasound probe is placed onthe head of a patient, and is then used to generate an ultrasound pulsewhich propagates through the skull and brain of the patient. Theultrasound pulse is reflected off of the skull and soft tissue lying ina path perpendicular to the ultrasound probe. A portion of a generatedEcho EG signal is then selected, and the Echo EG signal is integratedover the selected portion to generate an echopulsograph (“EPG”) signal.However, in order to determine ICP using the methods of the '743 patent,the operator must manually select, or “gate” a portion of the EPG andthen review the EPG waveforms at each gate to determine which providesthe optimal EPG waveform for a site of interest in the brain.

We have developed a novel method to noninvasively measure ICP and moregenerally brain elasticity that requires no manual review of EPG wavesby a technician. ICP is determined using an algorithm coupled on asimulated artificial neural network (“SANN”) that calculates ICP basedon a determination of a set of interacted ultrasound signals (“IUSs”)generated from multiple ultrasound pulses. The methods and systems ofthe present invention are capable of rapidly determining ICP withoutmanual review of EPG waves.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a non-invasivetechnique for measuring ICP based upon the analysis of reflectedultrasound signals represented in echopulsograph (“EPG”) form.

ICP is measured by first transmitting an ultrasound pulse of about atleast 1 MHz into the cranium of a patient. This ultrasound pulse is thenreflected by various structures in the cranium, including the walls ofthe third ventricle. The reflected signals are received by a transducer,and a package of information is generated.

The invention obtains multiple ultrasound signals of a patient. Sincethe state of the walls of the third ventricle are constantly changingdue to blood flow into and out of the brain (“systole and diastole”),the computer is able to compare each signal to locate the region of thethird ventricle based upon deviations in the respective waveforms.

Once the third ventricle is located, data points along the portion ofthe wave inside the third ventricle are used to calculate ICP. The ICPvalue is calculated from an algorithm that correlates the sampled valueswith ICP data derived from patients with known ICP values. Thecalculation is completed automatically by the computer once the systemhas been compared or trained by reference to known ICP values.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram of the preferred apparatus fortransmitting and receiving ultrasound waves and training the artificialneural network.

FIG. 2 depicts a flow chart of the intracranial pressure monitoringsystem.

FIG. 3 depicts one full ultrasound reflected signal (“USRS”).

FIG. 4 depicts the correlation between parts of the USRS and the partsof brain.

FIG. 5 depicts flow chart of inputting the USRS data points into theneural network and the algorithm to obtain an ICP value.

FIG. 6 depicts examples of the specific EPG points used as input.

FIG. 7 is a side-by-side comparison a QRS complex and ultrasoundsignals.

FIG. 8 depicts a flow chart of the back propagation method used in thetraining process.

FIG. 9 depicts an example of how the training process creates the rangeof measurable ICP values.

FIG. 10 depicts an embodiment of the hardware for the training process.

DETAILED DESCRIPTION

The present invention is directed to a method for non-invasivelymeasuring ICP and more generally the elasticity of tissues within orproximate to various organs or cavities within the body. In oneembodiment, ICP is determined by insonating the cranial cavity using atranscranial Doppler signal. First, the position of the anterior andposterior walls of the third ventricle are identified, and an ICP waveplot established. The ICP is then calculated from the ICP wave using aneural network. More generally, the methods and systems of the presentinvention may be used for measuring tissue elasticity in a variety ofdifferent tissues.

In one embodiment of the invention, the methods and systems of thepresent invention use ultrasonic probes. Such probes may be constructedfrom one or more piezoelectric elements activated by electrodes, forexample, from lead zirconate titanate (“PZT”), polyvinylidene diflouride(“PVDF”), PZT ceramic/polymer composite, and the like. The electrodesare connected to a voltage source, a voltage waveform is applied, andthe piezoelectric elements change in size at a frequency correspondingto that of the applied voltage. When a voltage waveform is applied, thepiezoelectric elements emit an ultrasonic wave into the media to whichit is coupled at the frequencies contained in the excitation waveform.Conversely, when an ultrasonic wave strikes the piezoelectric element,the element produces a corresponding voltage across its electrodes. Theinvention may be practiced using any of numerous ultrasonic probes thatare well known in the art.

FIG. 2 provides an overview of the methods of the invention. Anultrasound probe 1, transmits an ultrasound wave into the cranialcavity. The ultrasound probe is placed on the head of a patient, and isthen used to generate an ultrasound pulse which propagates through theskull and brain of the patient. The ultrasound pulse is reflected off ofthe occipital portion of the cranium 9 as well as off of othersemi-rigid or rigid structures encountered during transit across thebrain tissue 8. One such structure that is encountered by the ultrasoundpulse during transit is the third ventricle, including the anterior andposterior walls of the ventricle. The ultrasound pulse is reflected backto the ultrasound probe 1 to the ultrasound acquisition system 7. Anycommercially available ultrasound apparatus may be used with the methodsand systems of the present invention (see, Advanced Transducer Services,Inc. [online], [retrieved on Jul. 30, 2008]. Retrieved from the Internet<URL: www.atsultrasound.com/>). The signal can be interpreted by themicroprocessor system with a loaded algorithm 6, which identifies theposition of the third ventricular wall and correlates EPG points to anICP value.

FIG. 1 represents one embodiment of the system that can be used tomeasure the ICP. The system includes any central processing unit (“CPU”)or microprocessor system, such as a laptop computer 6, a universalserial bus (“USB”) interface 5, a digital signal processor (“DSP”) 4, anamplifier, an analog to digital converter (“ADC”) 3, an ultrasoundcircuit 2 and an ultrasound probe 1, having a transmitter, a receiverand a probe for generating the ultrasound pulse. The system isintegrated with a means for measuring heart beats. It will beappreciated that the embodiment shown in FIG. 1 represents only onesample configuration of the system of the invention having a CPU 6, ananalog to digital converter 3 and an ultrasound probe 1. All of thesecomponents are commercially available from standard electronicsuppliers.

Standard, commercially available components may be used in system of thepresent invention. The following description of specific components isonly exemplary, and the system of the present invention is not limitedto these components. For example, the DSP 4 may be a C2000 DSC andTMS320C20x by Texas Instruments, a Canberra's 2060 model, CEVA-X1641,CEVA-X1622, CEVA-X1620, or the CEVA-TeakLite-III. The DSP 4 isresponsible for generation of electrical pulses or signals with afrequency of at least 1 MHz via the probe 1, detection of the reflectedwaves or echoes through the probe 1, and processing of the detecteddigital signals. The ranges can be changed in the firmware of the DSP 4according to the signal studied.

A measurement cycle is initiated when a start signal from the computer 6is received by the DSP 4. In response, the DSP 4 instructs the probe 1to generate a series of ultrasound pulses. A commercially availableultrasound probe may be used with the methods and systems of theinvention (see, Advanced Transducer Services, Inc. [online], [retrievedon Jul. 30, 2008]. Retrieved from the Internet <URL:www.atsultrasound.com/>). The ultrasound probe 1 should be capable oftransmitting ultrasound waves at a frequency of at least about 1 MHz,and up to about 10 MHz.

Ultrasound sources and detectors may be employed in a transmission mode,or in a variety of reflection or scatter modes, including modes thatexamine the transference of pressure waves into shear waves, and viceversa. Ultrasound detection techniques may also be used to monitor theacoustic emission (“s”) from insonified tissue. Detection techniquesinvolve measurement of changes in acoustic scatter such as backscatter,or changes in acoustic emission. Examples of acoustic scatter oremission data that are related to tissue properties include changes inthe amplitude of acoustic signals, changes in phase of acoustic signals,changes in frequency of acoustic signals, changes in length of scatteredor emitted signals relative to the interrogation signal, changes in theprimary and/or other maxima and/or minima amplitudes of an acousticsignal within a cardiac and/or respiratory cycle; the ratio of themaximum and/or minimum amplitude to that of the mean or variance ordistribution of subsequent oscillations within a cardiac cycle, changesin temporal or spatial variance of scattered or emitted signals atdifferent times in the same location and/or at the same time indifferent locations, all possible rates of change of endogenous braintissue displacement or relaxation, such as the velocity or accelerationof displacement, and the like. Multiple acoustic interrogation signalsmay be employed, at the same or different frequencies, pulse lengths,pulse repetition frequencies, intensities, and the multipleinterrogation signals may be sent from the same location or multiplelocations simultaneously and/or sequentially. Scatter or emission fromsingle or multiple interrogation signals may be detected at single or atmultiple frequencies, at single or multiple times, and at single ormultiple locations.

FIG. 3 shows a single ultrasound reflected signal (“USRS”). Graphically,this ultrasound signal is referred to as an echopulsograph or EPG 10. Itis an interactive signal that indicates the anatomic position of theanterior and posterior cranial vaults and the intracranial contents inthe path of the ultrasound pulse. The ultrasound signals that insonatethe brain, including the third ventricle, possess a certain frequencycharacteristic. If the return signal is unchanged, the EPG is merelymeasuring anatomic structures and reflecting back the same wave form.However, if the insonated ultrasound signal interacts with everything inits path, particularly the third ventricle dynamics, the resultingwaveform or EPG is interactive and can be filtered to obtain a set ofreflected signals to calculate ICP. For example, FIG. 4 is a labeledinteractive EPG. The recognizable portions of the waveform correspond toreflected signals (“a”) inside the probe 11, (“b”) of the anteriorcranial vault, dura and meninges 12, (“c”) of the brain 13, (“d”) of thethird ventricle 14, and (“e”) the reflected signal of dura and theposterior cranial vault 15.

During any cardiac cycle (“systole and diastole”) multiple EPGmeasurements can be taken; FIG. 7 is a side-by-side comparison of EPGsand a QRS complex showing the relationship between the cardiac cycle andthe brain. Walls of the third ventricle expand and contract during thecardiac cycle (“systole and diastole”). Therefore, the positions of thewalls of the third ventricle vary relative to the ultrasound probeduring the cardiac cycle.

In one embodiment of the invention, at least 10 EPGs measurements aremade. In another embodiment, at least 25 EPGs are made. In a thirdembodiment, at least 50 EPGs are made. In a fourth embodiment, at least100 EPGs are made. The EPG signals are each digitized and displayed on adisplay screen as a function of intensity and time. As shown in FIG. 5,points from the third ventricle region of all the EPGs 16-19 created areinputted into an algorithm to calculate an ICP value. These points arerepresented more clearly in FIG. 6, which depicts how the thirdventricle region of an EPG is divided into insular points 21-35 overtime (“t”).

These points represent the discrete bundles of digitized data pointsfrom the isolated portion of the EPG, which are then used to calculateICP based on the equation:ICP=Σ tan h(“Σ^(I×W+b)”)W+bwhere I represents the input matrix of all the data points from theselected portion of the echopulsogram 21-35, W is the weight matrix thatis obtained through the training process, and b is a random biasconstant assigned by the computer 6.

The input matrix is a (“n by k”) mathematical matrix where n rows equalsthe number of samples; in one embodiment of this invention, this valueis at least ten. The k columns equal the data points along therespective EPGs found between the ventricle walls. The matrix iscalculated via known mathematical means.

The W value, or weight matrix, is obtained through the training orcorrelation process, which must be done once. The method of training theSANN is described in V. D. De Viterbo and J. C. Belchior, ArtificialNeural Networks Applied for Studying Metallic Complexes, Journal ofComputational Chemistry, vol. 22, no. 14, 1691-1701 (“2001”). Thetraining process is a backpropagation algorithm that consists ofrepeatedly presenting the input and desired output sets to the network.The weights are gradually corrected until the desired error is achievedin the network. This method is depicted in FIG. 8. In one embodiment ofthe invention, the backpropagation method is carried out according toΔW ^(l) _(ji)=ηδ_(j) ^(l)out_(i) ^(l−1) +μΔW _(ji) ^(l(previous))  (1)where ΔW^(l) _(ji) represents the correction to the weight between thejth element in the lth layer and ith element in the previous layer. Thequantity out _(I) ^(l−1) contains the output result on the l−1 layer.The parameters η and μ are denominated the learning rate and themomentum constant, respectively. These constants determine the rate ofconvergence during the training procedure. Usually, these parameters aredynamically adjusted to obtain the best convergence rate. The errorsintroduced during the training stage are calculated asδ_(i) ^(last)=(y _(j)−out_(j) ^(last))out_(j) ^(last)(1−out_(j) ^(last))

  (2)andδ_(j) ^(l)=(Σ_(k=1) ^(r) δkl+1Wkjl+1)

out_(j) ^(l)(1−out_(j) ^(l))  (3)where y_(j) is the output target that is compared with the outputresults of the out_(j) ^(l) of the lth layer. The network error can bethen calculated asε^(l)=Σ_(j=1) ^(n)(y _(j)−out_(j) ^(l))²  (4)For the learning procedure the neuron behavior was calculated throughthe sigmoid function for the intermediary layer and a linear function inthe output layer.

For minimizing functions, one embodiment of the invention uses therobust method proposed by Levenberg and implemented by Marquardt(Marquardt et al. J Soc Ind Appl Math 11:431 (“1963”). It works throughthe dynamical adjustment of the Steepest Descent method and Newton'smethod. Its advantage is that it is much faster in the way of findingthe minimum. According to the Levenberg-Marquardt method (LMM), theupdate matrix of the weights can be calculated asW _(n+1) =W _(n)−(H+BI)⁻¹∇ε¹(W _(n))  (5)where H is the Hessian matrix and β is a variable parameter, and usuallyit starts as β=0.01. The latter is changed during the minimizationsearch according to the estimation of the local error, and I is theidentity matrix. The most difficult task when the LMM is used can beattributed to the calculation of H, and it is approached byH=J ^(T) J  (6)where J is the Jacobian matrix and is given by

$\begin{matrix}{J = \frac{{\partial ɛ}\; l}{\partial{out}_{j}^{1}}} & (7)\end{matrix}$where l is the relative error of all weights [eq. (4)]. Thisapproximation for solving the Hessian matrix will avoid computation ofsecond derivatives, which simplifies the calculations. Substituting theabove approaches into eq. (5), one obtainsW _(n+1) =W _(n) −[J ^(T)(W _(n))J(W _(n))+β_(n) I] ⁻¹ J ^(T)(W_(n))ε¹(W _(n))  (8)Equation (8) will approach to the pure Gauss-Newton method if β→0 or tothe Steepest descent method when β→∞.

In accordance with the present invention, this means that, initially, anICP value is calculated via the equation with a randomly assigned Wvalue. The resulting ICP value is the test value. A reference ICP valueis determined by a known invasive means of measuring ICP. Training theninvolves comparing that test ICP value to the reference ICP valueobtained from a known invasive method. If the difference in ICP valuesis greater than an acceptable error, the random W value is adjusted.Upon adjusting the W value, a new test ICP value is calculated using theequation and this value is again compared to the reference ICP value.This training process of adjusting the weight value, calculating a newICP value and comparing it to a reference point is repeated until thecalculated ICP value from this process is within an acceptable range oferror to the reference value. When this occurs, the W value is stored bythe computer 6 and automatically correlated to that specific ICP valuethat was obtained as the test ICP value. In one embodiment of theinvention, the algorithm to train the neural network is as follows:

BEGIN WHILE START=ON GET SAMPLES OF DIGITALIZED ECHO FROM ADC STORE THESAMPLES IN A FILE PLOT THE SAMPLES CHOOSE THE VALID WAVES (MANUALPROCESS) IF WAVES ARE VALID START=OFF (MANUAL) END IF END WHILE NUMBERSOF INPUT OF THE NEURAL NETWORK=306 NUMBERS OF HIDDEN NEURONS=2W1(2×306)=RANDOM NUMBERS W2(1×2)=RANDOM NUMBERS WHILE(ERROR>0.001)ICP_NON_INVASIVE= W2*(TANH(W1*DIGITALIZED_ECHO)) ERROR=ICP_INVASIVE −ICP_NON_INVASIVE CALCULATE THE NEW W1 AND W2 USING THE LEVENBERGMARQUARDT METHOD W1=W1+DW1 W2=W2+DW2 END WHILE END BEGIN

This training process must be completed for each possible ICP value forthe computer to create an index or database of weight values andcorresponding ICP values. After the training, the computer 6 is able tocalculate the ICP values automatically by corresponding the appropriateW value for each set of inputs and ICP value without an invasiveprocedure. FIG. 9 illustrates how the training process expands the rangeof possible measured ICP values. Obtaining the ICP values of 9 patients,3 groups of 3, with 3 different ICP values and inputting their would beultrasound data into the invention as an initial matter provides theinvention with a baseline for comparison. The operating range of theinvention would also be equal to the range of the known ICP values itwas trained on.

The neural network is, therefore, an Algorithm for Correlation ofDynamic Properties of the Head (“ACDPH”) 20. It creates ICP waves usingthe inputted data. Each point at time (“t”) along the EPG wave is thenplotted across multiple EPG waves. As can be appreciated, up to (“n”)samples can be made from a single EPG wave. A graph is then prepared foreach time (“t”) showing the amplitude of the EPG wave at each time (“t”)for multiple EPG waves. For structures, such as the occipital portion ofthe cranium, which do not vary over the cardiac cycle, the graph showingthe sampling from multiple EPG waves at time (“t”) is a straight line.The same is not true of points along the third ventricle. Graphically,this is reflected by a change in amplitude in the EPG wave during thecycle. More specifically, this change is represented as an ICP wave witha sine wave pattern, reflecting the expansion and contraction of thewall over the cardiac cycle. The ADCPH 20 obtains the upper and lowerboundaries of the inputted points and correlates that data with thevalue of patients' ICPs obtained from an invasive device throughtraining. After training, the ADCPH is able to calculate the ICP of thepatient automatically without using an invasive method. In oneembodiment of the invention, the algorithm to obtain the ICP values isas follows:

BEGIN WHILE START=ON LOAD TRAINED NEURAL NETWORK W1 AND W2 GET SAMPLESOF DIGITALIZED ECHO FROM ADC STORE THE SAMPLES IN A FILE PLOT THESAMPLES CHOOSE THE VALID WAVES (MANUAL PROCESS) IF WAVES ARE VALID(MANUAL PROCESS) ICP_NON_INVASIVE= W2*(TANH(W1*DIGITIZED_ECHO)) END IFEND WHILE END BEGIN

FIG. 10 depicts one embodiment of the hardware for the instant trainingprocess. The data from the invasive ICP monitoring hardware, the datafrom the non-invasive ICP monitoring hardware, and the electrocardiogram(EKG) data are inputted into the DSP, which is then connected to alaptop computer through a USB interface. For data output, in oneembodiment of the invention, a laptop computer displays on its monitorthe EPG, the EKG, and the calculated non-invasive ICP values.

In contrast to the '743 patent, the present invention provides a moreaccurate ICP measurement because it takes into account the changes overtime in the third ventricle. The '743 patent relies on a point in timeat which the flow of blood through the brain tissue is primarily exitingthe brain. Moreover, after generating an EPG from an Echo EG signal andan ECG, the prior art patent relies on the operator to select theportion of the EPG that corresponds to the ICP value. In the presentinvention, the computer program identifies the relevant portion of thegraph, the third ventricle. Last, the '743 patent calculates ICP basedon an equation, ICP=ρ(“t/T”)*[t/T]−β, that relies on four differentequations to define p(“t/T”).

The scope of the present invention is not limited by what has beenspecifically shown and described hereinabove. Numerous references,including patents and various publications, are cited and discussed inthe description of this invention. The citation and discussion of suchreferences is provided merely to clarify the description of the presentinvention and is not an admission that any reference is prior art to theinvention described herein. All references cited and discussed in thisspecification are incorporated herein by reference in their entirety.Variations, modifications and other implementations of what is describedherein will occur to those of ordinary skill in the art withoutdeparting from the spirit and scope of the invention. While certainembodiments of the present invention have been shown and described, itwill be obvious to those skilled in the art that changes andmodifications may be made without departing from the spirit and scope ofthe invention. The matter set forth in the foregoing description andaccompanying drawings is offered by way of illustration only and not asa limitation. The actual scope of the invention is intended to bedefined in the following claims.

What is claimed is:
 1. A method of measuring elasticity of a tissuewithin a mammalian body, comprising the steps of: a. transmitting atleast one ultrasound pulse into the body at a target to obtain areflected signal; b. graphing the reflected signal (“s”) intensity overtime (“t”) to generate an echopulsograph (“EPG”); c. identifying thepoints of variation of said signal over time; and d. calculating theelasticity of a tissue using said points of variation and a weighingfunction W; wherein said weighing function W is obtained by performingthe steps of: e. determining a reference value corresponding to aninitial tissue elasticity as represented by an initial set of points ofvariation; f. assigning an arbitrary value for said weighing function W;g. calculating an intermediate value for a current tissue elasticityfrom said initial set of points of variation and said arbitrary value;h. calculating a difference between said reference value and saidintermediate value; i. changing said arbitrary value of function W basedon said difference; and j. repeating steps g and l until saidintermediate value reaches said reference value.
 2. The method of claim1, wherein the ultrasound pulse has a frequency of at least 1 MHz. 3.The method of claim 1, wherein the ultrasound pulse has a frequency ofat least 5 MHz.
 4. The method of claim 1, wherein the ultrasound pulsehas a frequency of at least 10 MHz.
 5. The method of claim 1, wherein atleast 10 ultrasound pulses are transmitted into the body.
 6. The methodof claim 1, where the ultrasound pulse has an amplitude between 0 to 200volts.
 7. The method of claim 1, where the ultrasound pulse is receivedby an ultrasound receiver at a rate of 10 mega-samples/sec over 14beats.
 8. The method of claim 1, where the ultrasound pulse is receivedby an ultrasound receiver at a rate of 1000 mega-samples/sec over 14beats.
 9. The method of claim 1, where the ultrasound pulse is receivedby an ultrasound receiver at a rate of 10,000 mega-samples/sec over 14beats.
 10. The method of claim 1, where the ultrasound pulse is receivedby an ultrasound receiver at a rate of 100,000 mega-samples/sec over 14beats.
 11. The method of claim 1 wherein said elasticity is calculatingusing the formula:Σ tan h(Σl×W+b)W+b wherein l is an input matrix of data points based onthe reflected signal(s), and b is a bias constant.
 12. The method ofclaim 1 wherein said step of calculating said elasticity is performedusing a neural network.
 13. The method of claim 1 further comprisingperforming an invasive technique on said tissue to determine saidreference value.